/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$

import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.evaluation.RegressionEvaluator;
import org.apache.spark.ml.feature.VectorIndexer;
import org.apache.spark.ml.feature.VectorIndexerModel;
import org.apache.spark.ml.regression.GBTRegressionModel;
import org.apache.spark.ml.regression.GBTRegressor;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$

public class JavaGradientBoostedTreeRegressorExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaGradientBoostedTreeRegressorExample")
                .getOrCreate();

        // $example on$
        // Load and parse the data file, converting it to a DataFrame.
        Dataset<Row> data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");

        // Automatically identify categorical features, and index them.
        // Set maxCategories so features with > 4 distinct values are treated as continuous.
        VectorIndexerModel featureIndexer = new VectorIndexer()
                .setInputCol("features")
                .setOutputCol("indexedFeatures")
                .setMaxCategories(4)
                .fit(data);

        // Split the data into training and test sets (30% held out for testing).
        Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        Dataset<Row> trainingData = splits[0];
        Dataset<Row> testData = splits[1];

        // Train a GBT model.
        GBTRegressor gbt = new GBTRegressor()
                .setLabelCol("label")
                .setFeaturesCol("indexedFeatures")
                .setMaxIter(10);

        // Chain indexer and GBT in a Pipeline.
        Pipeline pipeline = new Pipeline().setStages(new PipelineStage[]{featureIndexer, gbt});

        // Train model. This also runs the indexer.
        PipelineModel model = pipeline.fit(trainingData);

        // Make predictions.
        Dataset<Row> predictions = model.transform(testData);

        // Select example rows to display.
        predictions.select("prediction", "label", "features").show(5);

        // Select (prediction, true label) and compute test error.
        RegressionEvaluator evaluator = new RegressionEvaluator()
                .setLabelCol("label")
                .setPredictionCol("prediction")
                .setMetricName("rmse");
        double rmse = evaluator.evaluate(predictions);
        System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);

        GBTRegressionModel gbtModel = (GBTRegressionModel) (model.stages()[1]);
        System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString());
        // $example off$

        spark.stop();
    }
}
